There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

A review on carbon nanotubes and graphene as fillers in reinforced polymer nanocomposites

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

http://ieeexplore.ieee.org/iel5/5/31324/01456901.pdf

Microelectronic circuits

We have reviewed the humidity sensors based on ceramic materials. We first discuss the operating principle of ceramic humidity sensors. This is followed by a section on the relationship between the conduction mechanism and microstructure characteristics of the sensing elements of ceramic humidity sensors. This part of the review is also focused on the methods for optimization of the microstructure of ceramic porous elements. The next section summarizes the information on the materials used for the ceramic humidity sensors fabrication and effect of dopants or hybrid compositions on the sensing ceramic-based materials. Then we analyze state-of-the-art techniques for producing ceramic sensors. The key research and technological challenges in the field are discussed at the end of the review. The review is based on 424 references published during from 1998–2013.

Recent trends of ceramic humidity sensors development: A review

A simple single chip integrated moisture sensor capable of wireless operation through inductive coupling has been proposed. The structure consists of planar inductor and parallel plate capacitor fabricated by sol-gel thin film of γ-Al2O3 (5 μm thick). The sensor chip is capable of measuring ppm level moisture in gaseous environment. The resonance frequency shift of the sensor in the presence of moisture excited by a high frequency ac signal is measured by impedance analyzer (4294A Agilent). The frequency sensitivity of the structure for a moisture range from 100 to 5000 ppm is nearly 119 Hz/ppm. The response and recovery times are 7 and 24 s, respectively. The experimental results show that the output is linear (4.2%), reproducible and stable. Device can be applied for wireless moisture measurement in ppm level.

A Single Chip Integrated Sol-Gel Thin Film LC Sensor for Measuring Moisture in ppm Level

To obtain the solutions to the problem of linearization of the sensor characteristics is a matter of investigations for a long time. This paper presents a new and simple piecewise linearizing circuit to compensate the nonlinearity of highly nonlinear sensor. It is having a mixed signal conditioning circuit, employing a 2-bit flash ADC, a ( 4 × 1 ) multiplexer and the Op amp-based signal conditioning units. The circuit was initially simulated using SPICE software and then hardware implemented for linearizing the nonlinear response characteristic of two sensors. One of the sensors is a graphene oxide capacitive humidity sensor with nearly 46% nonlinearity and the other is a thermistor-based temperature sensor with 16.4 % nonlinearity. The humidity sensor is fabricated and characterized to measure humidity in the range of 2–85% RH. The maximum nonlinearity of the humidity and temperature sensors of the hardware implemented circuit after compensation was 1% and 1.5 % respectively. The circuit is simple and requires few hardware components for implementation and applicable for any nonlinear sensor. The circuit is CMOS compatible for ASIC implementation.

An efficient signal conditioning circuit to piecewise linearizing the response characteristic of highly nonlinear sensors

This paper represents the study of the effect of temperature on different capacitive humidity sensors used in practice. Capacitance of the humidity sensor, which is a function of concentration of water vapor, also depends on ambient temperature. This variation of ambient temperature causes error in the performance of sensor outputs and its compensation is essential. In this paper, we have used an artificial neural network to compensate the effect of ambient temperature error. The proposed artificial neural network technique is based on inverse model of the sensor. The technique is applicable for compensation of linear or nonlinear temperature effect of humidity sensor. It can also compensate the nonlinearity of the capacitive humidity response which is an issue for all most all types of humidity sensor. Our simulation studies show the sensor output and artificial neural network model output matches closely. Even though sensor characteristics change with temperature, the proposed model performs well irrespective of any change in temperature. It can be extended for the temperature compensation of other sensors. The maximum error for nonlinearity using the ANN technique are 0.2 % and temperature error of 0.08 % for temperature range between 10 °C to 60 °C of Sensor 3 and 0.01 % for temperature range between 25 °C to 85 °C of Sensor 4 respectively. Copyright © 2015 IFSA Publishing, S. L.

Temperature Effect on Capacitive Humidity Sensors and its Compensation Using Artificial Neural Networks

The size-selective laser-induced etching of semi-insulating GaAs 〈1 0 0〉 is carried out to create a porous structure by varying the laser beam exposure time. The etch-time dependent photoluminescence (PL) spectroscopy results show that a direct tuning of the size of the nanocrystallites has a limit and it can be effectively carried out till the oxides layers developed on the top surface make a damping in the redox reaction process. The etch rate ∼0.3–0.5 nm/s is observed in the initial state and it continued with much slower rate for longer etching duration. The formation of GaAs nanostructures as well as chemical reaction byproducts on the top surface is observed by atomic force microscopy (AFM) and scanning electron microscopy (SEM). An estimate of the etch-time dependent nanocrystallite size and its size distribution on the etched layer is obtained by using the quantum confinement model.

Size-selective laser-induced etching of semi-insulating GaAs: Photoluminescence studies

A simple signal conditioning circuit for converting capacitance change into frequency for capacitive humidity sensors is presented. It is based on a relaxation oscillator in which output frequency is linearly related to the capacitive unbalance of an active bridge. The design, analysis, and experimental results of the circuit and its application to a sol gel thin film porous γ-Al 2 O 3 based humidity sensor are reported. Experimental results confirm the theoretical value predicted. The circuit covering wide capacitance measurement range has the potential for remotely monitoring measurement parameters accurately.

Tarikul Islam

Papers

A Single Chip Integrated Sol-Gel Thin Film LC Sensor for Measuring Moisture in ppm Level

An efficient signal conditioning circuit to piecewise linearizing the response characteristic of highly nonlinear sensors

Temperature Effect on Capacitive Humidity Sensors and its Compensation Using Artificial Neural Networks

Size-selective laser-induced etching of semi-insulating GaAs: Photoluminescence studies

An oscillator based active bridge circuit for converting capacitance change into frequency for capacitive humidity sensor